AE2VID：基于光圈调制的事件视频重建
AE2VID: Event-based Video Reconstruction via Aperture Modulation
把光圈周期性调制当作主动事件源，在静态/低运动区域补出稠密事件，可直接用于事件相机高速视频重建与低纹理区域去噪；双分支分别处理运动触发与光圈触发事件的思路也可迁移到其他多模态事件融合任务。
原文依据 we periodically modulate the aperture to actively generate dense event signals, thereby encoding intensity cues even in static or low-motion regions
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论基于事件的视频重建旨在从事件流中恢复高速、高动态范围视频。现有方法完全依赖运动触发事件，而这类事件本身稀疏，主要捕捉动态区域，因此在事件稀少的区域常出现误差累积与质量下降。本文引入光圈调制触发事件作为互补机制，以丰富所捕捉的场景信息。具体而言，我们周期性地调制光圈以主动产生稠密事件信号，从而即使在静态或低运动区域也能编码强度线索。基于这一思想，我们设计了 AE2VID 框架，联合利用光圈调制触发事件与运动触发事件以提升预测的保真度。该框架包含两个子网络，分别专门处理两类事件。我们进一步采集了一个真实数据集并验证了方法的有效性。大量实验表明，我们的方法优于最优方法。代码与数据将发布于 https://github.com/a1henu/AE2VID/。
英文原文摘要 1154 字符
Event-based video reconstruction seeks to recover high-speed, high-dynamic-range videos from event streams. While existing approaches rely exclusively on motion-triggered events, these events are inherently sparse and primarily capture dynamic regions. Therefore, they often suffer from error accumulation and degraded quality in regions with few events. In this work, we introduce aperture-modulation-triggered events as a complementary mechanism to enrich the captured scene information. Specifically, we periodically modulate the aperture to actively generate dense event signals, thereby encoding intensity cues even in static or low-motion regions. Building upon this idea, we design an AE2VID framework that jointly leverages aperture-modulation-triggered and motion-triggered events to enhance the fidelity of predictions. The proposed framework consists of two subnetworks for the dedicated processing of both event types. We further collect a real dataset and validate the effectiveness of our method. Extensive experiments show our superiority over state-of-the-art methods. Code and data will be available at https://github.com/a1henu/AE2VID/.